Overview

Dataset statistics

Number of variables32
Number of observations3000000
Missing cells16062854
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 GiB
Average record size in memory760.1 B

Variable types

DateTime1
Categorical6
Numeric21
Text4

Alerts

AIRLINE is highly overall correlated with AIRLINE_CODE and 2 other fieldsHigh correlation
AIRLINE_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIRLINE_DOT is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIR_TIME is highly overall correlated with CANCELLED and 4 other fieldsHigh correlation
ARR_DELAY is highly overall correlated with CANCELLED and 2 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CANCELLED and 5 other fieldsHigh correlation
CANCELLATION_CODE is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
CANCELLED is highly overall correlated with AIR_TIME and 11 other fieldsHigh correlation
CRS_ARR_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_ELAPSED_TIME is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DELAY_DUE_CARRIER is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
DELAY_DUE_LATE_AIRCRAFT is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
DELAY_DUE_NAS is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
DELAY_DUE_SECURITY is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
DELAY_DUE_WEATHER is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DISTANCE is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DIVERTED is highly overall correlated with AIR_TIME and 8 other fieldsHigh correlation
DOT_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
ELAPSED_TIME is highly overall correlated with AIR_TIME and 4 other fieldsHigh correlation
TAXI_IN is highly overall correlated with CANCELLEDHigh correlation
WHEELS_OFF is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
WHEELS_ON is highly overall correlated with ARR_TIME and 5 other fieldsHigh correlation
CANCELLED is highly imbalanced (82.4%)Imbalance
DIVERTED is highly imbalanced (97.6%)Imbalance
DEP_TIME has 77615 (2.6%) missing valuesMissing
DEP_DELAY has 77644 (2.6%) missing valuesMissing
TAXI_OUT has 78806 (2.6%) missing valuesMissing
WHEELS_OFF has 78806 (2.6%) missing valuesMissing
WHEELS_ON has 79944 (2.7%) missing valuesMissing
TAXI_IN has 79944 (2.7%) missing valuesMissing
ARR_TIME has 79942 (2.7%) missing valuesMissing
ARR_DELAY has 86198 (2.9%) missing valuesMissing
CANCELLATION_CODE has 2920860 (97.4%) missing valuesMissing
ELAPSED_TIME has 86198 (2.9%) missing valuesMissing
AIR_TIME has 86198 (2.9%) missing valuesMissing
DELAY_DUE_CARRIER has 2466137 (82.2%) missing valuesMissing
DELAY_DUE_WEATHER has 2466137 (82.2%) missing valuesMissing
DELAY_DUE_NAS has 2466137 (82.2%) missing valuesMissing
DELAY_DUE_SECURITY has 2466137 (82.2%) missing valuesMissing
DELAY_DUE_LATE_AIRCRAFT has 2466137 (82.2%) missing valuesMissing
DELAY_DUE_SECURITY is highly skewed (γ1 = 101.2175459)Skewed
DEP_DELAY has 141944 (4.7%) zerosZeros
ARR_DELAY has 53685 (1.8%) zerosZeros
DELAY_DUE_CARRIER has 236912 (7.9%) zerosZeros
DELAY_DUE_WEATHER has 502435 (16.7%) zerosZeros
DELAY_DUE_NAS has 277386 (9.2%) zerosZeros
DELAY_DUE_SECURITY has 531104 (17.7%) zerosZeros
DELAY_DUE_LATE_AIRCRAFT has 274849 (9.2%) zerosZeros

Reproduction

Analysis started2025-12-11 05:13:15.263943
Analysis finished2025-12-11 05:19:33.564811
Duration6 minutes and 18.3 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

FL_DATE
Date

Distinct1704
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
Minimum2019-01-01 00:00:00
Maximum2023-08-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-11T00:19:33.676548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:34.043530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AIRLINE
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size219.1 MiB
Southwest Airlines Co.
576470 
Delta Air Lines Inc.
395239 
American Airlines Inc.
383106 
SkyWest Airlines Inc.
343737 
United Air Lines Inc.
254504 
Other values (13)
1046944 

Length

Max length34
Median length22
Mean length19.593653
Min length9

Characters and Unicode

Total characters58780958
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Air Lines Inc.
2nd rowDelta Air Lines Inc.
3rd rowUnited Air Lines Inc.
4th rowDelta Air Lines Inc.
5th rowSpirit Air Lines

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.576470
19.2%
Delta Air Lines Inc.395239
13.2%
American Airlines Inc.383106
12.8%
SkyWest Airlines Inc.343737
11.5%
United Air Lines Inc.254504
8.5%
Republic Airline143107
 
4.8%
Envoy Air121256
 
4.0%
JetBlue Airways112844
 
3.8%
Endeavor Air Inc.112463
 
3.7%
PSA Airlines Inc.107050
 
3.6%
Other values (8)450224
15.0%

Length

2025-12-11T00:19:34.162397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc1858158
20.1%
airlines1691504
18.3%
air1052545
11.4%
lines745454
8.1%
southwest576470
 
6.2%
co576470
 
6.2%
delta395239
 
4.3%
american383106
 
4.1%
skywest343737
 
3.7%
united254504
 
2.8%
Other values (18)1360141
14.7%

Most occurring characters

ValueCountFrequency (%)
i6754270
11.5%
6237328
 
10.6%
n5479504
 
9.3%
e5234759
 
8.9%
r3759928
 
6.4%
s3673652
 
6.2%
A3643361
 
6.2%
l2691744
 
4.6%
t2491261
 
4.2%
.2434628
 
4.1%
Other values (33)16380523
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)58780958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i6754270
11.5%
6237328
 
10.6%
n5479504
 
9.3%
e5234759
 
8.9%
r3759928
 
6.4%
s3673652
 
6.2%
A3643361
 
6.2%
l2691744
 
4.6%
t2491261
 
4.2%
.2434628
 
4.1%
Other values (33)16380523
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)58780958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i6754270
11.5%
6237328
 
10.6%
n5479504
 
9.3%
e5234759
 
8.9%
r3759928
 
6.4%
s3673652
 
6.2%
A3643361
 
6.2%
l2691744
 
4.6%
t2491261
 
4.2%
.2434628
 
4.1%
Other values (33)16380523
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)58780958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i6754270
11.5%
6237328
 
10.6%
n5479504
 
9.3%
e5234759
 
8.9%
r3759928
 
6.4%
s3673652
 
6.2%
A3643361
 
6.2%
l2691744
 
4.6%
t2491261
 
4.2%
.2434628
 
4.1%
Other values (33)16380523
27.9%

AIRLINE_DOT
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size230.6 MiB
Southwest Airlines Co.: WN
576470 
Delta Air Lines Inc.: DL
395239 
American Airlines Inc.: AA
383106 
SkyWest Airlines Inc.: OO
343737 
United Air Lines Inc.: UA
254504 
Other values (13)
1046944 

Length

Max length38
Median length26
Mean length23.593653
Min length13

Characters and Unicode

Total characters70780958
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Air Lines Inc.: UA
2nd rowDelta Air Lines Inc.: DL
3rd rowUnited Air Lines Inc.: UA
4th rowDelta Air Lines Inc.: DL
5th rowSpirit Air Lines: NK

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.: WN576470
19.2%
Delta Air Lines Inc.: DL395239
13.2%
American Airlines Inc.: AA383106
12.8%
SkyWest Airlines Inc.: OO343737
11.5%
United Air Lines Inc.: UA254504
8.5%
Republic Airline: YX143107
 
4.8%
Envoy Air: MQ121256
 
4.0%
JetBlue Airways: B6112844
 
3.8%
Endeavor Air Inc.: 9E112463
 
3.7%
PSA Airlines Inc.: OH107050
 
3.6%
Other values (8)450224
15.0%

Length

2025-12-11T00:19:34.248166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc1858158
15.2%
airlines1691504
13.8%
air1052545
 
8.6%
lines745454
 
6.1%
southwest576470
 
4.7%
wn576470
 
4.7%
co576470
 
4.7%
delta395239
 
3.2%
dl395239
 
3.2%
american383106
 
3.1%
Other values (36)3986673
32.6%

Most occurring characters

ValueCountFrequency (%)
9237328
 
13.1%
i6754270
 
9.5%
n5479504
 
7.7%
e5234759
 
7.4%
A4796658
 
6.8%
r3759928
 
5.3%
s3673652
 
5.2%
:3000000
 
4.2%
l2691744
 
3.8%
t2491261
 
3.5%
Other values (45)23661854
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)70780958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9237328
 
13.1%
i6754270
 
9.5%
n5479504
 
7.7%
e5234759
 
7.4%
A4796658
 
6.8%
r3759928
 
5.3%
s3673652
 
5.2%
:3000000
 
4.2%
l2691744
 
3.8%
t2491261
 
3.5%
Other values (45)23661854
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70780958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9237328
 
13.1%
i6754270
 
9.5%
n5479504
 
7.7%
e5234759
 
7.4%
A4796658
 
6.8%
r3759928
 
5.3%
s3673652
 
5.2%
:3000000
 
4.2%
l2691744
 
3.8%
t2491261
 
3.5%
Other values (45)23661854
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70780958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9237328
 
13.1%
i6754270
 
9.5%
n5479504
 
7.7%
e5234759
 
7.4%
A4796658
 
6.8%
r3759928
 
5.3%
s3673652
 
5.2%
:3000000
 
4.2%
l2691744
 
3.8%
t2491261
 
3.5%
Other values (45)23661854
33.4%

AIRLINE_CODE
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.8 MiB
WN
576470 
DL
395239 
AA
383106 
OO
343737 
UA
254504 
Other values (13)
1046944 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6000000
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowDL
3rd rowUA
4th rowDL
5th rowNK

Common Values

ValueCountFrequency (%)
WN576470
19.2%
DL395239
13.2%
AA383106
12.8%
OO343737
11.5%
UA254504
8.5%
YX143107
 
4.8%
MQ121256
 
4.0%
B6112844
 
3.8%
9E112463
 
3.7%
OH107050
 
3.6%
Other values (8)450224
15.0%

Length

2025-12-11T00:19:34.330076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn576470
19.2%
dl395239
13.2%
aa383106
12.8%
oo343737
11.5%
ua254504
8.5%
yx143107
 
4.8%
mq121256
 
4.0%
b6112844
 
3.8%
9e112463
 
3.7%
oh107050
 
3.6%
Other values (8)450224
15.0%

Most occurring characters

ValueCountFrequency (%)
A1153297
19.2%
O794524
13.2%
N672181
11.2%
W576470
9.6%
D395239
 
6.6%
L395239
 
6.6%
U254504
 
4.2%
Y208119
 
3.5%
9176929
 
2.9%
X163741
 
2.7%
Other values (12)1209757
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1153297
19.2%
O794524
13.2%
N672181
11.2%
W576470
9.6%
D395239
 
6.6%
L395239
 
6.6%
U254504
 
4.2%
Y208119
 
3.5%
9176929
 
2.9%
X163741
 
2.7%
Other values (12)1209757
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1153297
19.2%
O794524
13.2%
N672181
11.2%
W576470
9.6%
D395239
 
6.6%
L395239
 
6.6%
U254504
 
4.2%
Y208119
 
3.5%
9176929
 
2.9%
X163741
 
2.7%
Other values (12)1209757
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1153297
19.2%
O794524
13.2%
N672181
11.2%
W576470
9.6%
D395239
 
6.6%
L395239
 
6.6%
U254504
 
4.2%
Y208119
 
3.5%
9176929
 
2.9%
X163741
 
2.7%
Other values (12)1209757
20.2%

DOT_CODE
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19976.294
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:34.416851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19930
Q320368
95-th percentile20436
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation377.28462
Coefficient of variation (CV)0.018886617
Kurtosis-1.3107567
Mean19976.294
Median Absolute Deviation (MAD)374
Skewness-0.22978821
Sum5.9928882 × 1010
Variance142343.68
MonotonicityNot monotonic
2025-12-11T00:19:34.495613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19393576470
19.2%
19790395239
13.2%
19805383106
12.8%
20304343737
11.5%
19977254504
8.5%
20452143107
 
4.8%
20398121256
 
4.0%
20409112844
 
3.8%
20363112463
 
3.7%
20397107050
 
3.6%
Other values (8)450224
15.0%
ValueCountFrequency (%)
19393576470
19.2%
1968720634
 
0.7%
1969032114
 
1.1%
19790395239
13.2%
19805383106
12.8%
19930100467
 
3.3%
19977254504
8.5%
20304343737
11.5%
20363112463
 
3.7%
2036619082
 
0.6%
ValueCountFrequency (%)
20452143107
4.8%
2043664466
2.1%
2041695711
3.2%
20409112844
3.8%
20398121256
4.0%
20397107050
3.6%
2037865012
2.2%
2036852738
 
1.8%
2036619082
 
0.6%
20363112463
3.7%

FL_NUMBER
Real number (ℝ)

Distinct7111
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2511.5355
Minimum1
Maximum9562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:34.591353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile261
Q11051
median2152
Q33797
95-th percentile5656
Maximum9562
Range9561
Interquartile range (IQR)2746

Descriptive statistics

Standard deviation1747.258
Coefficient of variation (CV)0.69569314
Kurtosis-0.89109021
Mean2511.5355
Median Absolute Deviation (MAD)1334
Skewness0.50763183
Sum7.5346066 × 109
Variance3052910.7
MonotonicityNot monotonic
2025-12-11T00:19:34.699065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3341221
 
< 0.1%
641216
 
< 0.1%
4031196
 
< 0.1%
7061191
 
< 0.1%
3711185
 
< 0.1%
3521169
 
< 0.1%
5731147
 
< 0.1%
5391145
 
< 0.1%
6761138
 
< 0.1%
3581138
 
< 0.1%
Other values (7101)2988254
99.6%
ValueCountFrequency (%)
1931
< 0.1%
2886
< 0.1%
3866
< 0.1%
4843
< 0.1%
5663
< 0.1%
6856
< 0.1%
7792
< 0.1%
8753
< 0.1%
9720
< 0.1%
10655
< 0.1%
ValueCountFrequency (%)
95621
 
< 0.1%
88194
< 0.1%
88181
 
< 0.1%
88162
< 0.1%
88152
< 0.1%
88142
< 0.1%
88122
< 0.1%
88113
< 0.1%
88103
< 0.1%
88091
 
< 0.1%

ORIGIN
Text

Distinct380
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.7 MiB
2025-12-11T00:19:35.090282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9000000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFLL
2nd rowMSP
3rd rowDEN
4th rowMSP
5th rowMCO
ValueCountFrequency (%)
atl153556
 
5.1%
dfw130334
 
4.3%
ord122296
 
4.1%
den119919
 
4.0%
clt94304
 
3.1%
lax85872
 
2.9%
phx74815
 
2.5%
las73470
 
2.4%
sea70906
 
2.4%
mco63883
 
2.1%
Other values (370)2010645
67.0%
2025-12-11T00:19:35.539075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A1007533
 
11.2%
L834835
 
9.3%
S755056
 
8.4%
D715438
 
7.9%
T500379
 
5.6%
O457462
 
5.1%
C456042
 
5.1%
M401890
 
4.5%
F377357
 
4.2%
W359441
 
4.0%
Other values (16)3134567
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1007533
 
11.2%
L834835
 
9.3%
S755056
 
8.4%
D715438
 
7.9%
T500379
 
5.6%
O457462
 
5.1%
C456042
 
5.1%
M401890
 
4.5%
F377357
 
4.2%
W359441
 
4.0%
Other values (16)3134567
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1007533
 
11.2%
L834835
 
9.3%
S755056
 
8.4%
D715438
 
7.9%
T500379
 
5.6%
O457462
 
5.1%
C456042
 
5.1%
M401890
 
4.5%
F377357
 
4.2%
W359441
 
4.0%
Other values (16)3134567
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1007533
 
11.2%
L834835
 
9.3%
S755056
 
8.4%
D715438
 
7.9%
T500379
 
5.6%
O457462
 
5.1%
C456042
 
5.1%
M401890
 
4.5%
F377357
 
4.2%
W359441
 
4.0%
Other values (16)3134567
34.8%
Distinct373
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.6 MiB
2025-12-11T00:19:35.825314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.114709
Min length8

Characters and Unicode

Total characters39344126
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFort Lauderdale, FL
2nd rowMinneapolis, MN
3rd rowDenver, CO
4th rowMinneapolis, MN
5th rowOrlando, FL
ValueCountFrequency (%)
tx326015
 
4.7%
ca316863
 
4.5%
fl259713
 
3.7%
ga165211
 
2.4%
il164665
 
2.4%
chicago157368
 
2.3%
atlanta153556
 
2.2%
san150465
 
2.2%
ny146260
 
2.1%
new135441
 
1.9%
Other values (449)5007918
71.7%
2025-12-11T00:19:36.218435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3983475
 
10.1%
a3005775
 
7.6%
,3000000
 
7.6%
o2192431
 
5.6%
e2072173
 
5.3%
t1942800
 
4.9%
n1915018
 
4.9%
l1753494
 
4.5%
i1507440
 
3.8%
r1430006
 
3.6%
Other values (48)16541514
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)39344126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3983475
 
10.1%
a3005775
 
7.6%
,3000000
 
7.6%
o2192431
 
5.6%
e2072173
 
5.3%
t1942800
 
4.9%
n1915018
 
4.9%
l1753494
 
4.5%
i1507440
 
3.8%
r1430006
 
3.6%
Other values (48)16541514
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39344126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3983475
 
10.1%
a3005775
 
7.6%
,3000000
 
7.6%
o2192431
 
5.6%
e2072173
 
5.3%
t1942800
 
4.9%
n1915018
 
4.9%
l1753494
 
4.5%
i1507440
 
3.8%
r1430006
 
3.6%
Other values (48)16541514
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39344126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3983475
 
10.1%
a3005775
 
7.6%
,3000000
 
7.6%
o2192431
 
5.6%
e2072173
 
5.3%
t1942800
 
4.9%
n1915018
 
4.9%
l1753494
 
4.5%
i1507440
 
3.8%
r1430006
 
3.6%
Other values (48)16541514
42.0%

DEST
Text

Distinct380
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.7 MiB
2025-12-11T00:19:36.616586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9000000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowSEA
3rd rowMSP
4th rowSFO
5th rowDFW
ValueCountFrequency (%)
atl153569
 
5.1%
dfw129770
 
4.3%
ord123334
 
4.1%
den119592
 
4.0%
clt95413
 
3.2%
lax85621
 
2.9%
phx75605
 
2.5%
las73462
 
2.4%
sea70832
 
2.4%
mco63818
 
2.1%
Other values (370)2008984
67.0%
2025-12-11T00:19:37.069853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A1007429
 
11.2%
L834687
 
9.3%
S755323
 
8.4%
D714881
 
7.9%
T502444
 
5.6%
C458231
 
5.1%
O457402
 
5.1%
M400386
 
4.4%
F375758
 
4.2%
W358043
 
4.0%
Other values (16)3135416
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1007429
 
11.2%
L834687
 
9.3%
S755323
 
8.4%
D714881
 
7.9%
T502444
 
5.6%
C458231
 
5.1%
O457402
 
5.1%
M400386
 
4.4%
F375758
 
4.2%
W358043
 
4.0%
Other values (16)3135416
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1007429
 
11.2%
L834687
 
9.3%
S755323
 
8.4%
D714881
 
7.9%
T502444
 
5.6%
C458231
 
5.1%
O457402
 
5.1%
M400386
 
4.4%
F375758
 
4.2%
W358043
 
4.0%
Other values (16)3135416
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1007429
 
11.2%
L834687
 
9.3%
S755323
 
8.4%
D714881
 
7.9%
T502444
 
5.6%
C458231
 
5.1%
O457402
 
5.1%
M400386
 
4.4%
F375758
 
4.2%
W358043
 
4.0%
Other values (16)3135416
34.8%
Distinct373
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.6 MiB
2025-12-11T00:19:37.397957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.115468
Min length8

Characters and Unicode

Total characters39346403
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNewark, NJ
2nd rowSeattle, WA
3rd rowMinneapolis, MN
4th rowSan Francisco, CA
5th rowDallas/Fort Worth, TX
ValueCountFrequency (%)
tx325301
 
4.7%
ca316469
 
4.5%
fl260353
 
3.7%
il165395
 
2.4%
ga164952
 
2.4%
chicago158087
 
2.3%
atlanta153569
 
2.2%
san150105
 
2.2%
ny145339
 
2.1%
nc135112
 
1.9%
Other values (449)5006707
71.7%
2025-12-11T00:19:37.830747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3981389
 
10.1%
a3007585
 
7.6%
,3000000
 
7.6%
o2192461
 
5.6%
e2070816
 
5.3%
t1944131
 
4.9%
n1915428
 
4.9%
l1754841
 
4.5%
i1509323
 
3.8%
r1427774
 
3.6%
Other values (48)16542655
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)39346403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3981389
 
10.1%
a3007585
 
7.6%
,3000000
 
7.6%
o2192461
 
5.6%
e2070816
 
5.3%
t1944131
 
4.9%
n1915428
 
4.9%
l1754841
 
4.5%
i1509323
 
3.8%
r1427774
 
3.6%
Other values (48)16542655
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39346403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3981389
 
10.1%
a3007585
 
7.6%
,3000000
 
7.6%
o2192461
 
5.6%
e2070816
 
5.3%
t1944131
 
4.9%
n1915428
 
4.9%
l1754841
 
4.5%
i1509323
 
3.8%
r1427774
 
3.6%
Other values (48)16542655
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39346403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3981389
 
10.1%
a3007585
 
7.6%
,3000000
 
7.6%
o2192461
 
5.6%
e2070816
 
5.3%
t1944131
 
4.9%
n1915428
 
4.9%
l1754841
 
4.5%
i1509323
 
3.8%
r1427774
 
3.6%
Other values (48)16542655
42.0%

CRS_DEP_TIME
Real number (ℝ)

High correlation 

Distinct1384
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1327.062
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:37.941452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile605
Q1915
median1320
Q31730
95-th percentile2120
Maximum2359
Range2358
Interquartile range (IQR)815

Descriptive statistics

Standard deviation485.87885
Coefficient of variation (CV)0.36613124
Kurtosis-1.0361419
Mean1327.062
Median Absolute Deviation (MAD)410
Skewness0.08706432
Sum3.981186 × 109
Variance236078.26
MonotonicityNot monotonic
2025-12-11T00:19:38.073134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60063421
 
2.1%
70050527
 
1.7%
80031344
 
1.0%
90021357
 
0.7%
83020447
 
0.7%
63019645
 
0.7%
73019289
 
0.6%
100019200
 
0.6%
110018295
 
0.6%
170015904
 
0.5%
Other values (1374)2720571
90.7%
ValueCountFrequency (%)
124
 
< 0.1%
215
 
< 0.1%
31
 
< 0.1%
417
 
< 0.1%
5136
< 0.1%
636
 
< 0.1%
713
 
< 0.1%
816
 
< 0.1%
960
< 0.1%
1099
< 0.1%
ValueCountFrequency (%)
23593182
0.1%
2358210
 
< 0.1%
2357187
 
< 0.1%
2356207
 
< 0.1%
23551600
0.1%
2354154
 
< 0.1%
2353121
 
< 0.1%
2352148
 
< 0.1%
235182
 
< 0.1%
2350845
 
< 0.1%

DEP_TIME
Real number (ℝ)

High correlation  Missing 

Distinct1440
Distinct (%)< 0.1%
Missing77615
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1329.7759
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:38.199201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile602
Q1916
median1323
Q31739
95-th percentile2132
Maximum2400
Range2399
Interquartile range (IQR)823

Descriptive statistics

Standard deviation499.31005
Coefficient of variation (CV)0.37548436
Kurtosis-0.96882134
Mean1329.7759
Median Absolute Deviation (MAD)412
Skewness0.045195937
Sum3.8861172 × 109
Variance249310.53
MonotonicityNot monotonic
2025-12-11T00:19:38.328581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5557991
 
0.3%
5577186
 
0.2%
5567184
 
0.2%
6556662
 
0.2%
5586640
 
0.2%
5596214
 
0.2%
5546114
 
0.2%
6566048
 
0.2%
6575962
 
0.2%
6005812
 
0.2%
Other values (1430)2856572
95.2%
(Missing)77615
 
2.6%
ValueCountFrequency (%)
1381
< 0.1%
2286
< 0.1%
3222
< 0.1%
4267
< 0.1%
5265
< 0.1%
6233
< 0.1%
7253
< 0.1%
8250
< 0.1%
9223
< 0.1%
10240
< 0.1%
ValueCountFrequency (%)
2400242
< 0.1%
2359390
< 0.1%
2358413
< 0.1%
2357427
< 0.1%
2356475
< 0.1%
2355458
< 0.1%
2354532
< 0.1%
2353529
< 0.1%
2352515
< 0.1%
2351524
< 0.1%

DEP_DELAY
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1513
Distinct (%)0.1%
Missing77644
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean10.123326
Minimum-90
Maximum2966
Zeros141944
Zeros (%)4.7%
Negative1787569
Negative (%)59.6%
Memory size22.9 MiB
2025-12-11T00:19:38.442250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-90
5-th percentile-10
Q1-6
median-2
Q36
95-th percentile72
Maximum2966
Range3056
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.251835
Coefficient of variation (CV)4.865183
Kurtosis243.16697
Mean10.123326
Median Absolute Deviation (MAD)4
Skewness11.474159
Sum29583963
Variance2425.7432
MonotonicityNot monotonic
2025-12-11T00:19:38.576354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5240153
 
8.0%
-4223241
 
7.4%
-3212648
 
7.1%
-2191412
 
6.4%
-6186442
 
6.2%
-1168704
 
5.6%
-7153460
 
5.1%
0141944
 
4.7%
-8119359
 
4.0%
-988695
 
3.0%
Other values (1503)1196298
39.9%
ValueCountFrequency (%)
-901
< 0.1%
-891
< 0.1%
-871
< 0.1%
-821
< 0.1%
-741
< 0.1%
-731
< 0.1%
-682
< 0.1%
-661
< 0.1%
-622
< 0.1%
-571
< 0.1%
ValueCountFrequency (%)
29661
< 0.1%
29381
< 0.1%
29051
< 0.1%
29031
< 0.1%
28951
< 0.1%
28841
< 0.1%
26901
< 0.1%
25791
< 0.1%
25741
< 0.1%
25651
< 0.1%

TAXI_OUT
Real number (ℝ)

Missing 

Distinct179
Distinct (%)< 0.1%
Missing78806
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean16.643046
Minimum1
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:38.691047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile33
Maximum184
Range183
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1929012
Coefficient of variation (CV)0.55235691
Kurtosis23.34913
Mean16.643046
Median Absolute Deviation (MAD)4
Skewness3.4536312
Sum48617565
Variance84.509433
MonotonicityNot monotonic
2025-12-11T00:19:38.811758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12244243
 
8.1%
11236130
 
7.9%
13233843
 
7.8%
14214014
 
7.1%
10211251
 
7.0%
15191453
 
6.4%
16165427
 
5.5%
9162480
 
5.4%
17142434
 
4.7%
18121273
 
4.0%
Other values (169)998646
33.3%
ValueCountFrequency (%)
173
 
< 0.1%
2119
 
< 0.1%
3541
 
< 0.1%
42130
 
0.1%
56925
 
0.2%
623916
 
0.8%
757235
 
1.9%
8106409
3.5%
9162480
5.4%
10211251
7.0%
ValueCountFrequency (%)
1841
 
< 0.1%
1821
 
< 0.1%
1811
 
< 0.1%
1771
 
< 0.1%
1762
< 0.1%
1752
< 0.1%
1742
< 0.1%
1722
< 0.1%
1713
< 0.1%
1702
< 0.1%

WHEELS_OFF
Real number (ℝ)

High correlation  Missing 

Distinct1440
Distinct (%)< 0.1%
Missing78806
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1352.361
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:38.930406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile617
Q1931
median1336
Q31752
95-th percentile2145
Maximum2400
Range2399
Interquartile range (IQR)821

Descriptive statistics

Standard deviation500.87269
Coefficient of variation (CV)0.37036907
Kurtosis-0.9048526
Mean1352.361
Median Absolute Deviation (MAD)411
Skewness0.011307027
Sum3.9505088 × 109
Variance250873.45
MonotonicityNot monotonic
2025-12-11T00:19:39.055073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6085094
 
0.2%
6095001
 
0.2%
6104889
 
0.2%
6114824
 
0.2%
6124713
 
0.2%
6074663
 
0.2%
6134537
 
0.2%
7094453
 
0.1%
7104405
 
0.1%
7084401
 
0.1%
Other values (1430)2874214
95.8%
(Missing)78806
 
2.6%
ValueCountFrequency (%)
1599
< 0.1%
2453
< 0.1%
3434
< 0.1%
4494
< 0.1%
5459
< 0.1%
6445
< 0.1%
7481
< 0.1%
8453
< 0.1%
9472
< 0.1%
10449
< 0.1%
ValueCountFrequency (%)
2400401
< 0.1%
2359488
< 0.1%
2358441
< 0.1%
2357473
< 0.1%
2356457
< 0.1%
2355457
< 0.1%
2354458
< 0.1%
2353440
< 0.1%
2352442
< 0.1%
2351463
< 0.1%

WHEELS_ON
Real number (ℝ)

High correlation  Missing 

Distinct1440
Distinct (%)< 0.1%
Missing79944
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1462.4996
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:39.303409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile654
Q11049
median1501
Q31908
95-th percentile2246
Maximum2400
Range2399
Interquartile range (IQR)859

Descriptive statistics

Standard deviation527.23682
Coefficient of variation (CV)0.36050391
Kurtosis-0.43422672
Mean1462.4996
Median Absolute Deviation (MAD)417
Skewness-0.31554992
Sum4.2705806 × 109
Variance277978.66
MonotonicityNot monotonic
2025-12-11T00:19:39.453008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16293330
 
0.1%
16203268
 
0.1%
16263267
 
0.1%
16243260
 
0.1%
16273260
 
0.1%
16373238
 
0.1%
16423226
 
0.1%
16323223
 
0.1%
16253206
 
0.1%
16213193
 
0.1%
Other values (1430)2887585
96.3%
(Missing)79944
 
2.7%
ValueCountFrequency (%)
11505
0.1%
21273
< 0.1%
31265
< 0.1%
41288
< 0.1%
51238
< 0.1%
61205
< 0.1%
71172
< 0.1%
81141
< 0.1%
91116
< 0.1%
101138
< 0.1%
ValueCountFrequency (%)
24001109
< 0.1%
23591355
< 0.1%
23581439
< 0.1%
23571434
< 0.1%
23561428
< 0.1%
23551575
0.1%
23541536
0.1%
23531615
0.1%
23521616
0.1%
23511679
0.1%

TAXI_IN
Real number (ℝ)

High correlation  Missing 

Distinct202
Distinct (%)< 0.1%
Missing79944
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean7.6789822
Minimum1
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:39.577674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum249
Range248
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.2696393
Coefficient of variation (CV)0.81646749
Kurtosis59.921478
Mean7.6789822
Median Absolute Deviation (MAD)2
Skewness5.0793158
Sum22423058
Variance39.308377
MonotonicityNot monotonic
2025-12-11T00:19:39.693365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4453050
15.1%
5422185
14.1%
6343577
11.5%
3311862
10.4%
7275467
9.2%
8205931
6.9%
9158342
 
5.3%
10123961
 
4.1%
1196043
 
3.2%
291993
 
3.1%
Other values (192)437645
14.6%
(Missing)79944
 
2.7%
ValueCountFrequency (%)
15474
 
0.2%
291993
 
3.1%
3311862
10.4%
4453050
15.1%
5422185
14.1%
6343577
11.5%
7275467
9.2%
8205931
6.9%
9158342
 
5.3%
10123961
 
4.1%
ValueCountFrequency (%)
2491
< 0.1%
2441
< 0.1%
2401
< 0.1%
2352
< 0.1%
2331
< 0.1%
2321
< 0.1%
2291
< 0.1%
2251
< 0.1%
2221
< 0.1%
2171
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

High correlation 

Distinct1435
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1490.5607
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:39.816037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile725
Q11107
median1516
Q31919
95-th percentile2254
Maximum2400
Range2399
Interquartile range (IQR)812

Descriptive statistics

Standard deviation511.54757
Coefficient of variation (CV)0.34319138
Kurtosis-0.4738738
Mean1490.5607
Median Absolute Deviation (MAD)406
Skewness-0.27594661
Sum4.471682 × 109
Variance261680.91
MonotonicityNot monotonic
2025-12-11T00:19:39.945690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23599684
 
0.3%
19009065
 
0.3%
9508220
 
0.3%
21008166
 
0.3%
18107642
 
0.3%
10007619
 
0.3%
17107596
 
0.3%
14007579
 
0.3%
18457579
 
0.3%
20307520
 
0.3%
Other values (1425)2919330
97.3%
ValueCountFrequency (%)
1810
 
< 0.1%
2649
 
< 0.1%
3651
 
< 0.1%
4630
 
< 0.1%
52628
0.1%
6664
 
< 0.1%
7643
 
< 0.1%
8527
 
< 0.1%
9632
 
< 0.1%
102075
0.1%
ValueCountFrequency (%)
240014
 
< 0.1%
23599684
0.3%
23582950
 
0.1%
23572664
 
0.1%
23562403
 
0.1%
23555395
0.2%
23542033
 
0.1%
23531954
 
0.1%
23521786
 
0.1%
23511675
 
0.1%

ARR_TIME
Real number (ℝ)

High correlation  Missing 

Distinct1440
Distinct (%)< 0.1%
Missing79942
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1466.5112
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:40.059783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile653
Q11053
median1505
Q31913
95-th percentile2249
Maximum2400
Range2399
Interquartile range (IQR)860

Descriptive statistics

Standard deviation531.83835
Coefficient of variation (CV)0.36265551
Kurtosis-0.35873693
Mean1466.5112
Median Absolute Deviation (MAD)415
Skewness-0.35542043
Sum4.2822977 × 109
Variance282852.03
MonotonicityNot monotonic
2025-12-11T00:19:40.182456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16283246
 
0.1%
16203233
 
0.1%
16453232
 
0.1%
16313232
 
0.1%
16253229
 
0.1%
16333223
 
0.1%
16353220
 
0.1%
16323183
 
0.1%
16293181
 
0.1%
16243174
 
0.1%
Other values (1430)2887905
96.3%
(Missing)79942
 
2.7%
ValueCountFrequency (%)
11700
0.1%
21513
0.1%
31384
< 0.1%
41400
< 0.1%
51474
< 0.1%
61324
< 0.1%
71420
< 0.1%
81312
< 0.1%
91271
< 0.1%
101292
< 0.1%
ValueCountFrequency (%)
24001416
< 0.1%
23591570
0.1%
23581608
0.1%
23571763
0.1%
23561750
0.1%
23551749
0.1%
23541843
0.1%
23531780
0.1%
23521840
0.1%
23511912
0.1%

ARR_DELAY
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1527
Distinct (%)0.1%
Missing86198
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean4.2608582
Minimum-96
Maximum2934
Zeros53685
Zeros (%)1.8%
Negative1881970
Negative (%)62.7%
Memory size22.9 MiB
2025-12-11T00:19:40.291384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-96
5-th percentile-27
Q1-16
median-7
Q37
95-th percentile71
Maximum2934
Range3030
Interquartile range (IQR)23

Descriptive statistics

Standard deviation51.174824
Coefficient of variation (CV)12.01045
Kurtosis209.02717
Mean4.2608582
Median Absolute Deviation (MAD)10
Skewness10.293493
Sum12415297
Variance2618.8626
MonotonicityNot monotonic
2025-12-11T00:19:40.404800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1186647
 
2.9%
-1286584
 
2.9%
-1385598
 
2.9%
-1085400
 
2.8%
-984583
 
2.8%
-1482794
 
2.8%
-881987
 
2.7%
-1579719
 
2.7%
-779462
 
2.6%
-1676738
 
2.6%
Other values (1517)2084290
69.5%
(Missing)86198
 
2.9%
ValueCountFrequency (%)
-963
< 0.1%
-951
 
< 0.1%
-881
 
< 0.1%
-862
< 0.1%
-851
 
< 0.1%
-842
< 0.1%
-831
 
< 0.1%
-823
< 0.1%
-813
< 0.1%
-803
< 0.1%
ValueCountFrequency (%)
29341
< 0.1%
29131
< 0.1%
29121
< 0.1%
29111
< 0.1%
29002
< 0.1%
26851
< 0.1%
25681
< 0.1%
25651
< 0.1%
25601
< 0.1%
25561
< 0.1%

CANCELLED
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.7 MiB
0.0
2920860 
1.0
 
79140

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9000000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02920860
97.4%
1.079140
 
2.6%

Length

2025-12-11T00:19:40.517514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T00:19:40.584464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02920860
97.4%
1.079140
 
2.6%

Most occurring characters

ValueCountFrequency (%)
05920860
65.8%
.3000000
33.3%
179140
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05920860
65.8%
.3000000
33.3%
179140
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05920860
65.8%
.3000000
33.3%
179140
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05920860
65.8%
.3000000
33.3%
179140
 
0.9%

CANCELLATION_CODE
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing2920860
Missing (%)97.4%
Memory size182.7 MiB
B
28772 
D
24417 
A
19476 
C
6475 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters79140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowB
3rd rowD
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
B28772
 
1.0%
D24417
 
0.8%
A19476
 
0.6%
C6475
 
0.2%
(Missing)2920860
97.4%

Length

2025-12-11T00:19:40.652791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T00:19:40.721023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
b28772
36.4%
d24417
30.9%
a19476
24.6%
c6475
 
8.2%

Most occurring characters

ValueCountFrequency (%)
B28772
36.4%
D24417
30.9%
A19476
24.6%
C6475
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)79140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B28772
36.4%
D24417
30.9%
A19476
24.6%
C6475
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)79140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B28772
36.4%
D24417
30.9%
A19476
24.6%
C6475
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)79140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B28772
36.4%
D24417
30.9%
A19476
24.6%
C6475
 
8.2%

DIVERTED
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size171.7 MiB
0.0
2992944 
1.0
 
7056

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9000000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02992944
99.8%
1.07056
 
0.2%

Length

2025-12-11T00:19:40.816768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T00:19:40.886613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02992944
99.8%
1.07056
 
0.2%

Most occurring characters

ValueCountFrequency (%)
05992944
66.6%
.3000000
33.3%
17056
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05992944
66.6%
.3000000
33.3%
17056
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05992944
66.6%
.3000000
33.3%
17056
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05992944
66.6%
.3000000
33.3%
17056
 
0.1%

CRS_ELAPSED_TIME
Real number (ℝ)

High correlation 

Distinct640
Distinct (%)< 0.1%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean142.27581
Minimum1
Maximum705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:40.967364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile63
Q190
median125
Q3172
95-th percentile298
Maximum705
Range704
Interquartile range (IQR)82

Descriptive statistics

Standard deviation71.55669
Coefficient of variation (CV)0.50294348
Kurtosis2.5593398
Mean142.27581
Median Absolute Deviation (MAD)40
Skewness1.4327609
Sum4.2682543 × 108
Variance5120.3598
MonotonicityNot monotonic
2025-12-11T00:19:41.097051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9057102
 
1.9%
8555629
 
1.9%
8050520
 
1.7%
7546232
 
1.5%
7046057
 
1.5%
9544361
 
1.5%
11039177
 
1.3%
10537458
 
1.2%
11537140
 
1.2%
10036935
 
1.2%
Other values (630)2549375
85.0%
ValueCountFrequency (%)
11
 
< 0.1%
183
 
< 0.1%
2059
< 0.1%
2128
< 0.1%
2241
< 0.1%
2348
< 0.1%
2468
< 0.1%
2544
< 0.1%
2628
< 0.1%
279
 
< 0.1%
ValueCountFrequency (%)
7051
 
< 0.1%
6971
 
< 0.1%
6953
 
< 0.1%
6909
 
< 0.1%
68538
< 0.1%
6842
 
< 0.1%
6824
 
< 0.1%
6811
 
< 0.1%
68030
< 0.1%
6794
 
< 0.1%

ELAPSED_TIME
Real number (ℝ)

High correlation  Missing 

Distinct696
Distinct (%)< 0.1%
Missing86198
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean136.62054
Minimum15
Maximum739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:41.215734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile56
Q184
median120
Q3167
95-th percentile291
Maximum739
Range724
Interquartile range (IQR)83

Descriptive statistics

Standard deviation71.675816
Coefficient of variation (CV)0.52463425
Kurtosis2.497195
Mean136.62054
Median Absolute Deviation (MAD)40
Skewness1.4078756
Sum3.9808521 × 108
Variance5137.4225
MonotonicityNot monotonic
2025-12-11T00:19:41.337932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7923941
 
0.8%
8023458
 
0.8%
8323417
 
0.8%
8123354
 
0.8%
8223307
 
0.8%
7723280
 
0.8%
7823128
 
0.8%
7622872
 
0.8%
8422793
 
0.8%
8522749
 
0.8%
Other values (686)2681503
89.4%
(Missing)86198
 
2.9%
ValueCountFrequency (%)
153
 
< 0.1%
166
 
< 0.1%
1718
< 0.1%
1824
< 0.1%
1924
< 0.1%
2027
< 0.1%
2132
< 0.1%
2236
< 0.1%
2335
< 0.1%
2428
< 0.1%
ValueCountFrequency (%)
7391
< 0.1%
7221
< 0.1%
7201
< 0.1%
7191
< 0.1%
7181
< 0.1%
7161
< 0.1%
7142
< 0.1%
7131
< 0.1%
7121
< 0.1%
7101
< 0.1%

AIR_TIME
Real number (ℝ)

High correlation  Missing 

Distinct666
Distinct (%)< 0.1%
Missing86198
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean112.31084
Minimum8
Maximum692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:41.464593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q161
median95
Q3142
95-th percentile265
Maximum692
Range684
Interquartile range (IQR)81

Descriptive statistics

Standard deviation69.754843
Coefficient of variation (CV)0.62108737
Kurtosis2.5697142
Mean112.31084
Median Absolute Deviation (MAD)38
Skewness1.4409594
Sum3.2725155 × 108
Variance4865.7382
MonotonicityNot monotonic
2025-12-11T00:19:41.586268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6225471
 
0.8%
6325366
 
0.8%
6125171
 
0.8%
6425128
 
0.8%
6025055
 
0.8%
6524784
 
0.8%
5924720
 
0.8%
5624625
 
0.8%
5524619
 
0.8%
5724480
 
0.8%
Other values (656)2664383
88.8%
(Missing)86198
 
2.9%
ValueCountFrequency (%)
813
 
< 0.1%
991
 
< 0.1%
10101
 
< 0.1%
1149
 
< 0.1%
1253
 
< 0.1%
1391
 
< 0.1%
14104
 
< 0.1%
15256
 
< 0.1%
16567
< 0.1%
171004
< 0.1%
ValueCountFrequency (%)
6921
< 0.1%
6901
< 0.1%
6891
< 0.1%
6781
< 0.1%
6771
< 0.1%
6742
< 0.1%
6701
< 0.1%
6691
< 0.1%
6681
< 0.1%
6671
< 0.1%

DISTANCE
Real number (ℝ)

High correlation 

Distinct1727
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean809.36155
Minimum29
Maximum5812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:41.707943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile172
Q1377
median651
Q31046
95-th percentile2139
Maximum5812
Range5783
Interquartile range (IQR)669

Descriptive statistics

Standard deviation587.89394
Coefficient of variation (CV)0.72636751
Kurtosis2.843949
Mean809.36155
Median Absolute Deviation (MAD)318
Skewness1.4980542
Sum2.4280847 × 109
Variance345619.28
MonotonicityNot monotonic
2025-12-11T00:19:41.824411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33717618
 
0.6%
29613873
 
0.5%
39913655
 
0.5%
59412180
 
0.4%
22412138
 
0.4%
73311755
 
0.4%
40411635
 
0.4%
86211604
 
0.4%
21411574
 
0.4%
86711198
 
0.4%
Other values (1717)2872770
95.8%
ValueCountFrequency (%)
292
 
< 0.1%
3011
 
< 0.1%
31321
< 0.1%
4193
 
< 0.1%
435
 
< 0.1%
45184
< 0.1%
462
 
< 0.1%
5031
 
< 0.1%
5410
 
< 0.1%
6192
 
< 0.1%
ValueCountFrequency (%)
58121
 
< 0.1%
5095139
< 0.1%
4983322
< 0.1%
4962255
< 0.1%
490441
 
< 0.1%
4817120
 
< 0.1%
475746
 
< 0.1%
467851
 
< 0.1%
4502269
< 0.1%
447561
 
< 0.1%

DELAY_DUE_CARRIER
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1291
Distinct (%)0.2%
Missing2466137
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean24.759086
Minimum0
Maximum2934
Zeros236912
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:41.931097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323
95-th percentile104
Maximum2934
Range2934
Interquartile range (IQR)23

Descriptive statistics

Standard deviation71.771845
Coefficient of variation (CV)2.8988083
Kurtosis159.72182
Mean24.759086
Median Absolute Deviation (MAD)4
Skewness9.9998181
Sum13217960
Variance5151.1977
MonotonicityNot monotonic
2025-12-11T00:19:42.153733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236912
 
7.9%
159102
 
0.3%
19073
 
0.3%
28941
 
0.3%
38670
 
0.3%
68650
 
0.3%
48507
 
0.3%
168193
 
0.3%
58166
 
0.3%
78114
 
0.3%
Other values (1281)219535
 
7.3%
(Missing)2466137
82.2%
ValueCountFrequency (%)
0236912
7.9%
19073
 
0.3%
28941
 
0.3%
38670
 
0.3%
48507
 
0.3%
58166
 
0.3%
68650
 
0.3%
78114
 
0.3%
87653
 
0.3%
97237
 
0.2%
ValueCountFrequency (%)
29341
< 0.1%
29131
< 0.1%
29031
< 0.1%
28841
< 0.1%
26851
< 0.1%
25651
< 0.1%
25601
< 0.1%
25561
< 0.1%
25221
< 0.1%
23081
< 0.1%

DELAY_DUE_WEATHER
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct812
Distinct (%)0.2%
Missing2466137
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean3.9852603
Minimum0
Maximum1653
Zeros502435
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:42.264284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum1653
Range1653
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.410796
Coefficient of variation (CV)8.1326673
Kurtosis516.40848
Mean3.9852603
Median Absolute Deviation (MAD)0
Skewness19.223965
Sum2127583
Variance1050.4597
MonotonicityNot monotonic
2025-12-11T00:19:42.385004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0502435
 
16.7%
15678
 
< 0.1%
8613
 
< 0.1%
17596
 
< 0.1%
16595
 
< 0.1%
7580
 
< 0.1%
19567
 
< 0.1%
1566
 
< 0.1%
2566
 
< 0.1%
3556
 
< 0.1%
Other values (802)26111
 
0.9%
(Missing)2466137
82.2%
ValueCountFrequency (%)
0502435
16.7%
1566
 
< 0.1%
2566
 
< 0.1%
3556
 
< 0.1%
4503
 
< 0.1%
5545
 
< 0.1%
6550
 
< 0.1%
7580
 
< 0.1%
8613
 
< 0.1%
9552
 
< 0.1%
ValueCountFrequency (%)
16531
< 0.1%
14861
< 0.1%
14591
< 0.1%
14391
< 0.1%
14161
< 0.1%
13981
< 0.1%
13892
< 0.1%
13321
< 0.1%
13261
< 0.1%
13241
< 0.1%

DELAY_DUE_NAS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct671
Distinct (%)0.1%
Missing2466137
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean13.164728
Minimum0
Maximum1741
Zeros277386
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:42.500656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile55
Maximum1741
Range1741
Interquartile range (IQR)17

Descriptive statistics

Standard deviation33.161122
Coefficient of variation (CV)2.5189371
Kurtosis282.19185
Mean13.164728
Median Absolute Deviation (MAD)0
Skewness11.586589
Sum7028161
Variance1099.66
MonotonicityNot monotonic
2025-12-11T00:19:42.620639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0277386
 
9.2%
112458
 
0.4%
1510410
 
0.3%
29672
 
0.3%
169306
 
0.3%
38959
 
0.3%
48664
 
0.3%
178270
 
0.3%
58172
 
0.3%
187712
 
0.3%
Other values (661)172854
 
5.8%
(Missing)2466137
82.2%
ValueCountFrequency (%)
0277386
9.2%
112458
 
0.4%
29672
 
0.3%
38959
 
0.3%
48664
 
0.3%
58172
 
0.3%
67513
 
0.3%
77052
 
0.2%
86908
 
0.2%
96373
 
0.2%
ValueCountFrequency (%)
17411
< 0.1%
17111
< 0.1%
14681
< 0.1%
14411
< 0.1%
14031
< 0.1%
13431
< 0.1%
13061
< 0.1%
12941
< 0.1%
12831
< 0.1%
12721
< 0.1%

DELAY_DUE_SECURITY
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct172
Distinct (%)< 0.1%
Missing2466137
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean0.1459307
Minimum0
Maximum1185
Zeros531104
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:42.730287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1185
Range1185
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5820528
Coefficient of variation (CV)24.54626
Kurtosis24510.749
Mean0.1459307
Median Absolute Deviation (MAD)0
Skewness101.21755
Sum77907
Variance12.831102
MonotonicityNot monotonic
2025-12-11T00:19:42.845845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0531104
 
17.7%
15106
 
< 0.1%
16104
 
< 0.1%
993
 
< 0.1%
891
 
< 0.1%
1889
 
< 0.1%
586
 
< 0.1%
684
 
< 0.1%
1081
 
< 0.1%
1981
 
< 0.1%
Other values (162)1944
 
0.1%
(Missing)2466137
82.2%
ValueCountFrequency (%)
0531104
17.7%
150
 
< 0.1%
252
 
< 0.1%
363
 
< 0.1%
466
 
< 0.1%
586
 
< 0.1%
684
 
< 0.1%
779
 
< 0.1%
891
 
< 0.1%
993
 
< 0.1%
ValueCountFrequency (%)
11851
< 0.1%
3771
< 0.1%
3761
< 0.1%
3661
< 0.1%
3011
< 0.1%
3001
< 0.1%
2911
< 0.1%
2861
< 0.1%
2811
< 0.1%
2801
< 0.1%

DELAY_DUE_LATE_AIRCRAFT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct958
Distinct (%)0.2%
Missing2466137
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean25.471282
Minimum0
Maximum2557
Zeros274849
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2025-12-11T00:19:42.958845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile117
Maximum2557
Range2557
Interquartile range (IQR)30

Descriptive statistics

Standard deviation55.766892
Coefficient of variation (CV)2.1894026
Kurtosis118.51256
Mean25.471282
Median Absolute Deviation (MAD)0
Skewness7.3878286
Sum13598175
Variance3109.9462
MonotonicityNot monotonic
2025-12-11T00:19:43.081517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0274849
 
9.2%
156395
 
0.2%
165817
 
0.2%
175603
 
0.2%
185284
 
0.2%
195136
 
0.2%
205017
 
0.2%
214750
 
0.2%
144528
 
0.2%
134365
 
0.1%
Other values (948)212119
 
7.1%
(Missing)2466137
82.2%
ValueCountFrequency (%)
0274849
9.2%
13827
 
0.1%
23819
 
0.1%
33694
 
0.1%
43545
 
0.1%
53533
 
0.1%
63903
 
0.1%
73874
 
0.1%
84009
 
0.1%
94040
 
0.1%
ValueCountFrequency (%)
25571
< 0.1%
20961
< 0.1%
20101
< 0.1%
19051
< 0.1%
18721
< 0.1%
18021
< 0.1%
17361
< 0.1%
17221
< 0.1%
17151
< 0.1%
16692
< 0.1%

Interactions

2025-12-11T00:19:11.467969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:40.822489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:49.203869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:57.721514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:06.285604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:14.683621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:23.050397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:31.528905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:39.825314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:48.049601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:56.321025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:04.711526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:13.499242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:22.414175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:30.856216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:39.366586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:48.224422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:56.626736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:00.316885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:04.055564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:07.804498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:11.755944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:41.274767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:49.647682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:58.308942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:06.791206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:15.160300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:23.558041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:32.014605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:40.298049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:48.523381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:56.772847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:05.264048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:14.063727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:22.897870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:31.340919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:39.898119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:48.696114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:56.815219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:00.488394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:04.237042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:07.978034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:11.913587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:41.751491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:50.098476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:58.741784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:07.270968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:15.647042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:24.117542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:32.495321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:40.794720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:49.006024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:57.214981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:05.767746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:14.597328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:23.388554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:31.833603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:40.410748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:49.137127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:56.983814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:00.661957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:04.421549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:08.148578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:12.072525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:42.215225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:50.567224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:59.207854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:07.724743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:16.090809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:24.640111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:32.956073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:41.239530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:49.466791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:57.682684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:06.291356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:15.129874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:23.857302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:32.293374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:40.928364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:49.624822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:57.152318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:00.827522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:04.589099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:08.310144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:12.237549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:42.679031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:51.047934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:59.705476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:08.188675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:16.509734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:25.133792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:33.406836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:41.704333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:49.908563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:58.155071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:06.768036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:15.616618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:24.321028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:32.761074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:41.403141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:50.118501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:18:57.326853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:00.993078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:04.775601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:08.472709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:19:12.402077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:43.151765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:16:51.555578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:00.173224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:08.663454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:16.976471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:25.605563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T00:17:33.892581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-11T00:19:11.309380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-11T00:19:43.204197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AIRLINEAIRLINE_CODEAIRLINE_DOTAIR_TIMEARR_DELAYARR_TIMECANCELLATION_CODECANCELLEDCRS_ARR_TIMECRS_DEP_TIMECRS_ELAPSED_TIMEDELAY_DUE_CARRIERDELAY_DUE_LATE_AIRCRAFTDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_WEATHERDEP_DELAYDEP_TIMEDISTANCEDIVERTEDDOT_CODEELAPSED_TIMEFL_NUMBERTAXI_INTAXI_OUTWHEELS_OFFWHEELS_ON
AIRLINE1.0001.0001.0000.1650.0150.0470.2670.0460.0510.0490.1830.0240.0170.0220.0000.0190.0140.0470.1760.0091.0000.1650.4530.0200.0800.0490.048
AIRLINE_CODE1.0001.0001.0000.1650.0150.0470.2670.0460.0510.0490.1830.0240.0170.0220.0000.0190.0140.0470.1760.0091.0000.1650.4530.0200.0800.0490.048
AIRLINE_DOT1.0001.0001.0000.1650.0150.0470.2670.0460.0510.0490.1830.0240.0170.0220.0000.0190.0140.0470.1760.0091.0000.1650.4530.0200.0800.0490.048
AIR_TIME0.1650.1650.1651.0000.0340.0410.0001.0000.049-0.0320.9840.015-0.0880.1660.008-0.0330.081-0.0330.9861.000-0.0630.979-0.3350.1280.071-0.0390.043
ARR_DELAY0.0150.0150.0150.0341.0000.1100.0001.0000.1100.126-0.0270.2040.3520.002-0.0090.1290.6560.1640.0031.000-0.0360.099-0.0390.1170.2730.1690.116
ARR_TIME0.0470.0470.0470.0410.1101.0000.0001.0000.9000.7340.036-0.0750.130-0.005-0.0030.0080.1290.7550.0450.029-0.0030.041-0.001-0.0280.0220.7700.978
CANCELLATION_CODE0.2670.2670.2670.0000.0000.0001.0001.0000.0890.0920.0710.0000.0000.0000.0000.0000.0000.1540.0791.0000.1600.0000.1460.0000.0000.1530.000
CANCELLED0.0460.0460.0461.0001.0001.0001.0001.0000.0160.0180.0171.0001.0001.0001.0001.0000.0150.0100.0180.0080.0331.0000.0131.0000.0060.0061.000
CRS_ARR_TIME0.0510.0510.0510.0490.1100.9000.0890.0161.0000.7980.046-0.0580.204-0.051-0.0030.0080.1400.7960.0560.0110.0010.046-0.008-0.0370.0170.8040.907
CRS_DEP_TIME0.0490.0490.049-0.0320.1260.7340.0920.0180.7981.000-0.038-0.0400.250-0.101-0.0050.0040.1530.970-0.0270.0100.007-0.035-0.001-0.068-0.0020.9550.753
CRS_ELAPSED_TIME0.1830.1830.1830.984-0.0270.0360.0710.0170.046-0.0381.0000.032-0.0680.1090.008-0.0290.076-0.0380.9790.013-0.0220.974-0.3120.1660.112-0.0430.037
DELAY_DUE_CARRIER0.0240.0240.0240.0150.204-0.0750.0001.000-0.058-0.0400.0321.000-0.251-0.371-0.063-0.2350.305-0.0240.0421.000-0.054-0.043-0.019-0.119-0.138-0.035-0.072
DELAY_DUE_LATE_AIRCRAFT0.0170.0170.017-0.0880.3520.1300.0001.0000.2040.250-0.068-0.2511.000-0.291-0.016-0.0500.4570.281-0.0591.000-0.071-0.150-0.017-0.088-0.2030.2630.138
DELAY_DUE_NAS0.0220.0220.0220.1660.002-0.0050.0001.000-0.051-0.1010.109-0.371-0.2911.000-0.013-0.010-0.377-0.1170.0941.0000.1300.321-0.0600.2920.447-0.093-0.008
DELAY_DUE_SECURITY0.0000.0000.0000.008-0.009-0.0030.0001.000-0.003-0.0050.008-0.063-0.016-0.0131.000-0.0170.001-0.0050.0111.0000.0110.003-0.019-0.003-0.014-0.006-0.003
DELAY_DUE_WEATHER0.0190.0190.019-0.0330.1290.0080.0001.0000.0080.004-0.029-0.235-0.050-0.010-0.0171.0000.1090.015-0.0361.0000.057-0.0150.053-0.0040.0740.0160.008
DEP_DELAY0.0140.0140.0140.0810.6560.1290.0000.0150.1400.1530.0760.3050.457-0.3770.0010.1091.0000.1980.0890.011-0.1740.079-0.084-0.0500.0240.1940.137
DEP_TIME0.0470.0470.047-0.0330.1640.7550.1540.0100.7960.970-0.038-0.0240.281-0.117-0.0050.0150.1981.000-0.0280.0100.001-0.0340.001-0.0650.0040.9840.774
DISTANCE0.1760.1760.1760.9860.0030.0450.0790.0180.056-0.0270.9790.042-0.0590.0940.011-0.0360.089-0.0281.0000.013-0.0860.961-0.3540.1130.056-0.0350.048
DIVERTED0.0090.0090.0091.0001.0000.0291.0000.0080.0110.0100.0131.0001.0001.0001.0001.0000.0110.0100.0131.0000.0071.0000.0030.0180.0130.0100.029
DOT_CODE1.0001.0001.000-0.063-0.036-0.0030.1600.0330.0010.007-0.022-0.054-0.0710.1300.0110.057-0.1740.001-0.0860.0071.000-0.0060.3380.2140.2760.005-0.005
ELAPSED_TIME0.1650.1650.1650.9790.0990.0410.0001.0000.046-0.0350.974-0.043-0.1500.3210.003-0.0150.079-0.0340.9611.000-0.0061.000-0.3080.2120.203-0.0360.043
FL_NUMBER0.4530.4530.453-0.335-0.039-0.0010.1460.013-0.008-0.001-0.312-0.019-0.017-0.060-0.0190.053-0.0840.001-0.3540.0030.338-0.3081.000-0.0230.0860.008-0.005
TAXI_IN0.0200.0200.0200.1280.117-0.0280.0001.000-0.037-0.0680.166-0.119-0.0880.292-0.003-0.004-0.050-0.0650.1130.0180.2140.212-0.0231.0000.068-0.065-0.036
TAXI_OUT0.0800.0800.0800.0710.2730.0220.0000.0060.017-0.0020.112-0.138-0.2030.447-0.0140.0740.0240.0040.0560.0130.2760.2030.0860.0681.0000.0240.025
WHEELS_OFF0.0490.0490.049-0.0390.1690.7700.1530.0060.8040.955-0.043-0.0350.263-0.093-0.0060.0160.1940.984-0.0350.0100.005-0.0360.008-0.0650.0241.0000.789
WHEELS_ON0.0480.0480.0480.0430.1160.9780.0001.0000.9070.7530.037-0.0720.138-0.008-0.0030.0080.1370.7740.0480.029-0.0050.043-0.005-0.0360.0250.7891.000

Missing values

2025-12-11T00:19:15.758834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-11T00:19:20.014136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-11T00:19:29.985607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
02019-01-09United Air Lines Inc.United Air Lines Inc.: UAUA199771562FLLFort Lauderdale, FLEWRNewark, NJ11551151.0-4.019.01210.01443.04.015011447.0-14.00.0NaN0.0186.0176.0153.01065.0NaNNaNNaNNaNNaN
12022-11-19Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901149MSPMinneapolis, MNSEASeattle, WA21202114.0-6.09.02123.02232.038.023152310.0-5.00.0NaN0.0235.0236.0189.01399.0NaNNaNNaNNaNNaN
22022-07-22United Air Lines Inc.United Air Lines Inc.: UAUA19977459DENDenver, COMSPMinneapolis, MN9541000.06.020.01020.01247.05.012521252.00.00.0NaN0.0118.0112.087.0680.0NaNNaNNaNNaNNaN
32023-03-06Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902295MSPMinneapolis, MNSFOSan Francisco, CA16091608.0-1.027.01635.01844.09.018291853.024.00.0NaN0.0260.0285.0249.01589.00.00.024.00.00.0
42020-02-23Spirit Air LinesSpirit Air Lines: NKNK20416407MCOOrlando, FLDFWDallas/Fort Worth, TX18401838.0-2.015.01853.02026.014.020412040.0-1.00.0NaN0.0181.0182.0153.0985.0NaNNaNNaNNaNNaN
52019-07-31Southwest Airlines Co.Southwest Airlines Co.: WNWN19393665DALDallas, TXOKCOklahoma City, OK10101237.0147.015.01252.01328.03.011101331.0141.00.0NaN0.060.054.036.0181.0141.00.00.00.00.0
62023-06-11American Airlines Inc.American Airlines Inc.: AAAA198052134DCAWashington, DCBOSBoston, MA10101001.0-9.023.01024.01122.08.011591130.0-29.00.0NaN0.0109.089.058.0399.0NaNNaNNaNNaNNaN
72019-07-08Republic AirlineRepublic Airline: YXYX204524464HSVHuntsville, ALDCAWashington, DC16431637.0-6.022.01659.01927.041.019452008.023.00.0NaN0.0122.0151.088.0613.00.00.023.00.00.0
82023-02-12Spirit Air LinesSpirit Air Lines: NKNK20416590IAHHouston, TXLAXLos Angeles, CA530527.0-3.011.0538.0658.08.0717706.0-11.00.0NaN0.0227.0219.0200.01379.0NaNNaNNaNNaNNaN
92020-08-22Alaska Airlines Inc.Alaska Airlines Inc.: ASAS19930223SEASeattle, WAFAIFairbanks, AK21252116.0-9.019.02135.02353.03.023552356.01.00.0NaN0.0210.0220.0198.01533.0NaNNaNNaNNaNNaN
FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
29999902023-07-26SkyWest Airlines Inc.SkyWest Airlines Inc.: OOOO203044126DTWDetroit, MIMSNMadison, WI825824.0-1.032.0856.0851.05.0843856.013.00.0NaN0.078.092.055.0311.0NaNNaNNaNNaNNaN
29999912021-12-03Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901146MSPMinneapolis, MNSNASanta Ana, CA18251833.08.023.01856.02015.07.020302022.0-8.00.0NaN0.0245.0229.0199.01522.0NaNNaNNaNNaNNaN
29999922019-01-13JetBlue AirwaysJetBlue Airways: B6B6204091668CHSCharleston, SCBOSBoston, MA12581245.0-13.015.01300.01438.04.015101442.0-28.00.0NaN0.0132.0117.098.0818.0NaNNaNNaNNaNNaN
29999932019-12-23Allegiant AirAllegiant Air: G4G4203681899SRQSarasota/Bradenton, FLINDIndianapolis, IN907905.0-2.011.0916.01106.09.011251115.0-10.00.0NaN0.0138.0130.0110.0876.0NaNNaNNaNNaNNaN
29999942020-08-31Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901408FLLFort Lauderdale, FLLGANew York, NY700653.0-7.016.0709.0927.06.0944933.0-11.00.0NaN0.0164.0160.0138.01076.0NaNNaNNaNNaNNaN
29999952022-11-13American Airlines Inc.American Airlines Inc.: AAAA198051522JAXJacksonville, FLCLTCharlotte, NC17421740.0-2.010.01750.01845.06.019071851.0-16.00.0NaN0.085.071.055.0328.0NaNNaNNaNNaNNaN
29999962022-11-02American Airlines Inc.American Airlines Inc.: AAAA198051535ORDChicago, ILAUSAustin, TX13001254.0-6.010.01304.01514.05.015561519.0-37.00.0NaN0.0176.0145.0130.0977.0NaNNaNNaNNaNNaN
29999972022-09-11Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902745HSVHuntsville, ALATLAtlanta, GA534615.041.016.0631.0759.06.0729805.036.00.0NaN0.055.050.028.0151.00.036.00.00.00.0
29999982019-11-13Republic AirlineRepublic Airline: YXYX204526134BOSBoston, MALGANew York, NY16001555.0-5.019.01614.01704.08.017281712.0-16.00.0NaN0.088.077.050.0184.0NaNNaNNaNNaNNaN
29999992019-06-15Southwest Airlines Co.Southwest Airlines Co.: WNWN193932823LGBLong Beach, CASJCSan Jose, CA730727.0-3.09.0736.0828.02.0840830.0-10.00.0NaN0.070.063.052.0324.0NaNNaNNaNNaNNaN